README.md

metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - azaheadhealth
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: microtest-2.0
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: azaheadhealth
          type: azaheadhealth
          config: micro
          split: test
          args: micro
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.75
          - name: F1
            type: f1
            value: 0.8
          - name: Precision
            type: precision
            value: 0.6666666666666666
          - name: Recall
            type: recall
            value: 1

microtest-2.0

This model is a fine-tuned version of bert-base-uncased on the azaheadhealth dataset. It achieves the following results on the evaluation set:

• Loss: 0.3672
• Accuracy: 0.75
• F1: 0.8
• Precision: 0.6667
• Recall: 1.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 2
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.8113	0.5	1	0.4486	0.75	0.8	0.6667	1.0
0.7227	1.0	2	0.3672	0.75	0.8	0.6667	1.0

Framework versions

• Transformers 4.31.0
• Pytorch 2.2.0+cu121
• Datasets 2.16.1
• Tokenizers 0.13.2